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TEXTRUNNER

TEXTRUNNER. Banko , M., Cafarella , M. J., Soderland . S., Broadhead , M., & Etzioni O. (2007). Open Information Extraction from the Web. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007 )

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TEXTRUNNER

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  1. TEXTRUNNER Banko, M., Cafarella, M. J., Soderland. S., Broadhead, M., & Etzioni O. (2007). Open Information Extraction from the Web. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007) Cafarella, M. J., Banko, M., & Etzioni, Oren. (2006). Relational Web Search. UW CSE Tech Report 2006-04-02 Yates, A., & Etzioni, O. (2007). Unsupervised Resolution of Objects and Relations on the Web. NAACL-HLT2007 Turing Center Computer Science and Engineering University of Washington Reporter: Yi-Ting Huang Date: 2009/9/4

  2. PART 1. Query Qualified List Queries Relationship Queries Unnamed-Item Queries Factoid Queries

  3. Relationship Queries

  4. Factoid Queries

  5. Qualified List Queries

  6. PART 2. Retrieval Tn=(ei, r, ej)

  7. PART 3. clustering

  8. corpus PART 2 input • Query Processing • Learner • Buildinvertedindex • Extractor • Assessor • a subset of extractions • a set of raw triple • a structured set of extractions PART 3 output Relationship Queries Factoid Queries Qualified List Queries Unnamed-Item Queries input PART 1 output

  9. Spreading Activation Search B (80%) D (50%) F (20%) A(100%) C (80%) E (50%) G (20%) decay factor Spreading activation is a technique that has been used to perform associative retrieval of nodes in a graph.

  10. PART1: Scoring based on Spreading Activation Search TextHit(ni; qj) =1, If a node ni contains a query term qj, TextHit(ni; qj) = 0, Otherwise. TextHit(e; qj ) = 1, If an edge e contains a query term qj, TextHit(e; qj ) = 0, Otherwise. decay factor:0~1 search query term Q={q0, q1,…qn-1 }e.g. king of pop, Q={q0=king, q1=of, q3=pop}

  11. A ranked list, T1={q0 q1…,e1,n12} T2={q0 q1…,e2,n22} T3={q0 q1…,e3,n32} …. Q={q0, q1, … qn-1}

  12. PART 2. Input & Output • Input (corpus): • Given a corpus of 9 million Web pages • containing 133 million sentences, • Output: • extracted a set of 60.5 million tuples • an extraction rate of 2.2 tuples per sentence.

  13. Learner Positive example Training example Dependency parser Heuristics Rule Falseexample Learner

  14. Dependency parser • Dependency parsers locate instances of semantic relationships between words, forming a directed graph that connects all words in the input. • the subject relation (John ← hit), • the object relation (hit → ball) • phrasal modification (hit → with → bat).

  15. S=(Mary is a good student who lives in Taipei.)T=(Mary/NP1 is/VB a good student/NP2 who/PP lives/VB in/PP Taipei/NP3.) e1 is NP, e.g. Mary e.g. Taipei e.g. who lives in e.g. positive / negative e.g. |R|=3<M e.g. lives/VB e.g. (Mary, Taipei,T)

  16. S=(Mary is a good student who lives in Taipei.)T=(Mary/NP1 is/VB a good student/NP2 who/PP lives/VB in/PP Taipei/NP3.) A=(Mary/NP1 is/VB a good student/NP2 who/PP lives/VB in/PP Taipei/NP3.) e.g.Mary is subject e.g.Mary is head e.g.(Mary, Taipei, T) e.g.if “Taipei” is object of PP, then if “Taipei” is valid semantic role then positive else negative else positive, R=normalize(R ) e.g. lives—> live

  17. Learner • Naive Bayes classifier • T(ei, ri,j ,ej )Features include • the presence of part-of-speech tag sequences in the relation ri,j , • the number of tokens in ri,j , • the number of stopwords in ri,j, • whether or not an object e is found to be a proper noun, • the part-of-speech tag to the left of ei, • the part-of-speech tag to the right of ej .

  18. Unsupervised Resolution of Objects and Relations on the Web Alexander Yates, Oren Etzioni Turing Center Computer Science and Engineering University of Washington Proceedings of NAACL HLT 2007

  19. Research Motivation • Web Information Extraction (WIE) systems extract assertions that describe a relation and its arguments from Web text. • (is capital of ,D.C.,United States) • (is capital city of ,Washington,U.S.) which describes the same relationship as above but contains a different name for the relation and each argument. • We refer to the problem of identifying synonymous object and relation names as Synonym Resolution (SR).

  20. Research purpose we present RESOLVER, a novel, domain-independent, unsupervised synonym resolution system that applies to both objects and relations. RESOLVER Elements co-referential names together using a probabilistic model informed by string similarity and the similarity of the assertions containing thenames.

  21. Assessor • SSM • ESP • CombineEvidence • Clustering • A structured subset of extractions • a set of extractions output input

  22. String Similarity Model (SSM) • T=(s, r, o); • s and o are object string; • r is relation string. • (r,s) is the property of s. • (s,o) is the instance of r. • If s1 and s2 are object string, sim(s1, s2) based on Monge-Elkan string similarity. • If s1 and s2 are relation string, sim(s1, s2) based on Levenshtein string distance.

  23. Levenshtein string distance • Food • Good • God • Good

  24. Extracted Shared Property Model (ESP) Mars Red planet 659 26 k=4 |Ej|=nj |Ei|=ni 3500 |Ui|=Pi 659 |Ei|=ni • T=(s, r, o); • s and o are object string; • r is relation string. • (r,s) is the property of s. • (si, sj), si=Mars; sj=red planet • (Mars, lacks, ozone layer) 659 • (red Planet, lacks, ozone layer) 26 • They share four properties 4

  25. Ball and urns abstraction ESP uses a pair of urns, containing Pi and Pjballs respectively, for the two strings si and sj . Some subset of the Pi balls have the exact same labels as an equal-sized subset of the Pj balls. Let the size of this subset be Si,j.

  26. Sj Si Sij |Uj|=Pj |Ui|=Pi |Fj|=s |Fi|=r |Ej|=nj k |Ei|=ni

  27. Ball and urns abstraction

  28. Sij Sij Pi ni

  29. Sij r s Sj Si Sij |Uj|=Pj |Ui|=Pi |Fj|=s |Fi|=r |Ej|=nj k |Ei|=ni

  30. Combine Evidence

  31. e1=(dog, live, house) e2=(puppy, live, house) e3=(cat, live, house) e4=(cat, live, home) e5=(kitty, live, home) Elements[dog]=1 Elements[live]=2 Elements[house]=3 Elements[puppy]=4 Elements[cat]=5 Elements[home]=6 Elements[kitty]=7 Elements[1]=dog Elements[2]=live Elements[3]=house Elements[4]=puppy Elements[5]cat Elements[6]=home Elements[7]kitty Max=50 1 round Index[live house]=(1,4,5, live house) Index[live home]=(5,7, live home) Index[cat live]=(3,6, cat live) Sim(1,4) Sim(1,5) Sim(4,5) Sim(5,7) Sim(3,6)

  32. e1=(dog, live, house) e2=(puppy, live, house) e3=(cat, live, house) e4=(cat, live, home) e5=(kitty, live, home) Elements[dog]=1 Elements[live]=2 Elements[house]=3 Elements[puppy]=1 Elements[cat]=5 Elements[home]=6 Elements[kitty]=7 Elements[1]=dog+puppy Elements[2]=live Elements[3]=house Elements[4]=puppy Elements[5]cat Elements[6]=home Elements[7]kitty Max=50 1 round Index[live house]=(1,4,5, live house) Index[live home]=(5,7, live home) Index[cat live]=(3,6, cat live) Sim(1,4) Sim(1,5) Sim(4,5) Sim(5,7) Sim(3,6) UsedCluster={}

  33. e1=(dog, live, house) e2=(puppy, live, house) e3=(cat, live, house) e4=(cat, live, home) e5=(kitty, live, home) Elements[dog]=1 Elements[live]=2 Elements[house]=3 Elements[puppy]=1 Elements[cat]=5 Elements[home]=3 Elements[kitty]=5 Elements[1]=dog+puppy Elements[2]=live Elements[3]=house+home Elements[4]=puppy Elements[5]cat+kitty Elements[6]=home Elements[7]kitty Max=50 1 round Index[live house]=(1,4,5, live house) Index[live home]=(5,7, live home) Index[cat live]=(3,6, cat live) Sim(1,4) Sim(1,5) Sim(4,5) Sim(5,7) Sim(3,6) UsedCluster={(1,4), (5,7), (3,6)}

  34. e1=(dog, live, house) e2=(puppy, live, house) e3=(cat, live, house) e4=(cat, live, home) e5=(kitty, live, home) Elements[dog]=1 Elements[live]=2 Elements[house]=3 Elements[puppy]=1 Elements[cat]=5 Elements[home]=3 Elements[kitty]=5 Elements[1]=dog+puppy Elements[2]=live Elements[3]=house+home Elements[4]=puppy Elements[5]cat+kitty Elements[6]=home Elements[7]kitty Max=50 2 round Index[live house]=(1,1,5, live house) Index[live home]=(5,5, live home) Index[cat live]=(3,3, cat live) UsedCluster={}

  35. e1=(dog, live, house) e2=(puppy, live, house) e3=(cat, live, house) e4=(cat, live, home) e5=(kitty, live, home) Elements[dog]=1 Elements[live]=2 Elements[house]=3 Elements[puppy]=1 Elements[cat]=1 Elements[home]=3 Elements[kitty]=5 Elements[1]=dog+puppy+cat Elements[2]=live Elements[3]=house+home Elements[4]=puppy Elements[5]cat+kitty Elements[6]=home Elements[7]kitty Max=50 2 round Index[live house]=(1,5, live house) Index[live home]=(5, live home) Index[cat live]=(3, cat live) Sim(1,5) UsedCluster={(1,5)}

  36. e1=(dog, live, house) e2=(puppy, live, house) e3=(cat, live, house) e4=(cat, live, home) e5=(kitty, live, home) Elements[dog]=1 Elements[live]=2 Elements[house]=3 Elements[puppy]=1 Elements[cat]=1 Elements[home]=3 Elements[kitty]=5 Elements[1]=dog+puppy+cat Elements[2]=live Elements[3]=house+home Elements[4]=puppy Elements[5]cat+kitty Elements[6]=home Elements[7]kitty Max=50 2 round Index[live house]=(1,5, live house) Index[live home]=(5, live home) Index[cat live]=(3, cat live) Sim(1,5) UsedCluster={(1,5)} (dog, puppy, cat)(live,house) (cat, kitty)(live,home) (cat, live)(house, home)

  37. Experiment • Dataset : • 9797 distinct object strings • 10151 distinct relation strings • Metric: • Measure the precision by manually labeling all of the cluster. • Measure the recall • The top 200 object strings formed 51 clusters of size, with an average cluster is size of 2.9. • For relation string, formed 110 clusters, with an avg cluster size of4.9.

  38. Result CSM had particular trouble with lower-frequency strings, judging ar too many of them to be co-referential on too little evidence. Extraction error Multiple word sense.

  39. Function Filtering (West Virginia, capital of, Richmond)(Virginia, capital of, Charleston) sim(y1,y2)>thdif there exist a function f and extraction f(x1,y1) and f(x2, y2) match (1 to 1)then not be merged. It requires as input the set of functional and one-to-one relations in the data

  40. Web Hitcounts • While names for two similar objects may often appear together in the same sentence, it is relatively rare for two different names of the same object to appear in the same sentence. • Coordination-Phrase Filter searches

  41. Experiment

  42. Conclusion In the study, it showed that how the TEXTRUNNER automatically extracts information from web and the RESOLVER system finds clusters of co-referential object names in the relations 78% and recall of 68% with the aid of CPF.

  43. Comments • The assumption of ESP model • How to use TextRunner in my research • Find relation • Using TextRunner as query expansion

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